This is the supplementary notes and examples for lecture 16.

It is based on data and examples seen in class

Built on the excellent tutorial from RStudio

If not already installed, the install leaflet:

install.packages("leaflet")

Then attach the library:

library(leaflet)

Bring in the other needed libraries:

library(sf)
library(tidyverse)
library(USAboundaries)

Basic Usage

Creating a Leaflet map requires a few basic steps (not unsimular to ggplot):

Initialize a map widget by calling leaflet().
Add layers (i.e., features) to the map by using layer functions (e.g. addTiles, addMarkers, addPolygons,…)
Print the map widget to display it.

Here’s a basic example:

leaflet() %>%
  addTiles() %>%
  addMarkers(lng=-119.8489, lat=34.4140, popup="UCSB")

By default, leaflet sets the view of the map to the range of latitude/longitude data in the map layers
You can adjust these if needed using:
- setView(): sets the center of the map view and the zoom level;
- fitBounds(): fits the view into the rectangle [lng1, lat1] – [lng2, lat2];
- clearBounds() clears the bound

Basemaps

Default (OpenStreetMap) Tiles

The easiest way to add tiles is by calling addTiles() with no arguments; by default, OpenStreetMap tiles are used.

(leaflet() %>% 
  setView(lng=-119.8489, lat=34.4140, zoom = 12) %>% 
  addTiles())

Third-Party Tiles

Many third-party basemaps can be added using the addProviderTiles() function
As a convenience, leaflet provides a named list of all the third-party tile providers supported by the plugin: just type providers$ and choose from one of the options.

length(providers)

#> [1] 164

names(providers) %>% 
  head()

#> [1] "OpenStreetMap"        "OpenStreetMap.Mapnik" "OpenStreetMap.DE"    
#> [4] "OpenStreetMap.CH"     "OpenStreetMap.France" "OpenStreetMap.HOT"

Note that some tile set providers require you to register. You can pass access tokens/keys, and other options, to the tile provider by populating the options argument with the providerTileOptions() function.

I would personal stick with the OSM, CartoDB, ESRI, and Stamen servers …

Below are a few examples:

CartoDB

leaflet() %>% 
  setView(lng=-119.8489, lat=34.4140, zoom = 12) %>% 
  addProviderTiles(providers$CartoDB)

Stamen.Toner

leaflet() %>% 
  setView(lng=-119.8489, lat=34.4140, zoom = 12) %>% 
  addProviderTiles(providers$Stamen.Toner)

ESRI Imagery

leaflet() %>% 
  setView(lng=-119.8489, lat=34.4140, zoom = 12) %>% 
  addProviderTiles(providers$Esri.WorldImagery)

Stamen.TopOSMRelief

leaflet() %>% 
  setView(lng=-119.8489, lat=34.4140, zoom = 12) %>% 
  addProviderTiles(providers$Stamen.TopOSMRelief)

Combining Tile Layers

You can stack multiple tile layers if the front tiles have some level of opacity. Here we layer the Stamen.TonerLines with aerial imagery

leaflet() %>% 
  setView(lng=-119.8489, lat=34.4140, zoom = 12) %>% 
  addProviderTiles(providers$Esri.WorldImagery) %>% 
   addProviderTiles(providers$Stamen.TonerLines, options = providerTileOptions(opacity = .5))

Markers

Markers are one way to identify point information on a map:

Example Starbucks data:

(starbucks = read_csv('../data/directory.csv') %>% 
  filter(City %in% c("Goleta", "Santa Barbara")) %>% 
  st_as_sf(coords = c("Longitude", "Latitude"), crs = 4326) %>% 
  select(store_name = `Store Name`, phone = `Phone Number`, address = `Street Address`, city = City, brand = Brand))

#> Simple feature collection with 11 features and 5 fields
#> geometry type:  POINT
#> dimension:      XY
#> bbox:           xmin: -119.87 ymin: 34.4 xmax: -119.65 ymax: 34.44
#> geographic CRS: WGS 84
#> # A tibble: 11 x 6
#>    store_name      phone   address         city    brand                geometry
#>    <chr>           <chr>   <chr>           <chr>   <chr>             <POINT [°]>
#>  1 Hollister & St… 805-96… 7030 Marketpla… Goleta  Starb…        (-119.87 34.43)
#>  2 Goleta          805-68… 173 N. Fairvie… Goleta  Starb…        (-119.83 34.44)
#>  3 Montecito       (805) … 1046 - A Coast… Santa … Starb…        (-119.65 34.42)
#>  4 State & Cota, … 805-88… 539 State Stre… Santa … Starb…         (-119.7 34.42)
#>  5 Ralph's - Sant… 805-56… 100 W Carrillo… Santa … Starb…         (-119.7 34.42)
#>  6 800 State Stre… (805) … 800 State Stre… Santa … Starb…         (-119.7 34.42)
#>  7 W Carrillo & D… 805-89… 208 West Carri… Santa … Starb…         (-119.7 34.42)
#>  8 La Cumbre Plaza 805-56… 3815 State Str… Santa … Starb…        (-119.75 34.44)
#>  9 Five Points Ce… (805) … 3957 State Str… Santa … Starb…        (-119.75 34.44)
#> 10 State & Victor… 805-88… 1235 State Str… Santa … Starb…        (-119.71 34.42)
#> 11 1990 Cliff Dri… 805-88… 1990 Cliff Dri… Santa … Starb…         (-119.72 34.4)

Markers

Markers are added using the addMarkers or addAwesomeMarkers

Their default appearance is a blue dropped pin.

As with most layer functions, - the popup argument adds a message to be displayed on click - the label argument display a text label either on hover

leaflet() %>% 
  addProviderTiles(providers$CartoDB) %>% 
  addMarkers(data = starbucks, popup = ~store_name, label = ~city)

Awesome Icons

Using the Font Awesome Icons seen in lab one, we can make markers with more specific coloring and icons

Here we define the icon as a green marker with a coffee icon from the fa library
For fun we can make the coffee cups spin…

We then use addAwesomeMarkers to spcifiy the icon we created using the icon argument:

icons = awesomeIcons(icon = 'coffee', markerColor = "green", library = 'fa', spin = TRUE)

leaflet(data = starbucks) %>% addProviderTiles(providers$CartoDB) %>% 
  addAwesomeMarkers(icon = icons, popup = ~store_name)

Custom Popups

You can use HTML, CSS, and Java Script to modify your pop-ups

For example, we can associate the name of the Starbucks locations with their google maps URL as an hyper reference (href):

starbucks = starbucks %>% 
  mutate(url = paste0('https://www.google.com/maps/place/',
                      gsub(" ", "+", address), "+",
                      gsub(" ", "+", city)))

pop = paste0('<a href=', starbucks$url, '>', starbucks$store_name, "</a>")
head(pop)

#> [1] "<a href=https://www.google.com/maps/place/7030+Marketplace+Drive+Goleta>Hollister & Storke-Goleta</a>"               
#> [2] "<a href=https://www.google.com/maps/place/173+N.+Fairview+Avenue+Goleta>Goleta</a>"                                  
#> [3] "<a href=https://www.google.com/maps/place/1046+-+A+Coast+Village+Road,+Paseo+Nuevo+Shops+Santa+Barbara>Montecito</a>"
#> [4] "<a href=https://www.google.com/maps/place/539+State+Street+Santa+Barbara>State & Cota, Santa Barbara</a>"            
#> [5] "<a href=https://www.google.com/maps/place/100+W+Carrillo+St+Santa+Barbara>Ralph's - Santa Barbara #680</a>"          
#> [6] "<a href=https://www.google.com/maps/place/800+State+Street+Santa+Barbara>800 State Street</a>"

We can then add our custom popup to our icons:

leaflet(data = starbucks) %>%
  addProviderTiles(providers$CartoDB) %>% 
  addAwesomeMarkers(icon = icons, 
                    label = ~address, popup = pop)

Circle Markers

Circle markers are much like regular circles (shapes), except their radius in onscreen pixels stays constant regardless of zoom level (z).

leaflet(data = starbucks) %>%
  addProviderTiles(providers$CartoDB) %>% 
  addCircleMarkers(label = ~address, popup = pop)

Marker Clustering

Sometimes while mapping many points, it is useful to cluster them. For example lets plot all starbucks in the world!

all_sb = read_csv('../data/directory.csv') %>% 
  filter(!is.na(Latitude)) %>% 
  st_as_sf(coords = c("Longitude", "Latitude"), crs = 4326) 

leaflet(data = all_sb) %>% 
  addProviderTiles(providers$CartoDB) %>% 
  addMarkers(clusterOptions = markerClusterOptions())

Adding color ramps

Colors can be add by factor, numeric, bins, or quartiles using the built in leaflet functions
Each of these are defined by a palette, and a domain

The palette argument can be any of the following:

A character vector of RGB or named colors.
- Examples: palette(), c(“#000000”, “#0000FF”, “#FFFFFF”), topo.colors(10)
The name of an RColorBrewer palette
- Examples: “BuPu” or “Greens”.
The full name of a viridis palette:
- Examples: “viridis”, “magma”, “inferno”, or “plasma”.
A function that receives a single value between 0 and 1 and returns a color.
- Examples: colorRamp(c(“#000000”, “#FFFFFF”), interpolate = “spline”).

The domain is the values - named by variable - that the color palette should range over

# ?colorFactor

# Create a palette that maps factor levels to colors
pal <- colorFactor(c("darkgreen", "navy"), domain = c("Goleta", "Santa Barbara"))

leaflet(data = starbucks) %>% addProviderTiles(providers$CartoDB) %>% 
  addCircleMarkers(color = ~pal(city), fillOpacity = .5, stroke = FALSE)

Shapes (Polylines, Polygons, Circles)

Getting some data …

(covid = readr::read_csv('https://raw.githubusercontent.com/nytimes/covid-19-data/master/live/us-states.csv') %>% 
  filter(date == max(date)) %>% 
  right_join(USAboundaries::us_states(), by = c("state" = "name")) %>% 
  filter(!stusps %in% c("AK","PR", "HI")) %>% 
  st_as_sf())

#> Simple feature collection with 49 features and 20 fields
#> geometry type:  MULTIPOLYGON
#> dimension:      XY
#> bbox:           xmin: -124.7258 ymin: 24.49813 xmax: -66.9499 ymax: 49.38436
#> geographic CRS: WGS 84
#> # A tibble: 49 x 21
#>    date       state         fips   cases deaths confirmed_cases confirmed_deaths
#>    <date>     <chr>         <chr>  <dbl>  <dbl>           <dbl>            <dbl>
#>  1 2021-03-12 Alabama       01    5.02e5  10274          393700             8069
#>  2 2021-03-12 Arizona       04    8.30e5  16464              NA               NA
#>  3 2021-03-12 Arkansas      05    3.26e5   5410          256519             4342
#>  4 2021-03-12 California    06    3.61e6  55132         3593106            55041
#>  5 2021-03-12 Colorado      08    4.43e5   6108              NA               NA
#>  6 2021-03-12 Connecticut   09    2.89e5   7761              NA               NA
#>  7 2021-03-12 Delaware      10    8.97e4   1494              NA               NA
#>  8 2021-03-12 District of … 11    4.21e4   1038           42128             1038
#>  9 2021-03-12 Florida       12    1.96e6  32039               0            32039
#> 10 2021-03-12 Georgia       13    1.00e6  17570          807746            15370
#> # … with 39 more rows, and 14 more variables: probable_cases <dbl>,
#> #   probable_deaths <dbl>, statefp <chr>, statens <chr>, affgeoid <chr>,
#> #   geoid <chr>, stusps <chr>, lsad <chr>, aland <dbl>, awater <dbl>,
#> #   state_name <chr>, state_abbr <chr>, jurisdiction_type <chr>,
#> #   geometry <MULTIPOLYGON [°]>

Polygons

Adding those cases counts to polygons over a YlOrRd color ramp

leaflet() %>% 
  addProviderTiles(providers$CartoDB) %>% 
  addPolygons(data = covid, 
              fillColor  = ~colorQuantile("YlOrRd", confirmed_cases)(confirmed_cases),
              color = NA,
              label = ~state_name)

Circles

leaflet() %>% 
  addProviderTiles(providers$CartoDB.DarkMatter) %>% 
  addCircles(data = st_centroid(covid), 
             fillColor  = ~colorQuantile("YlOrRd", cases)(cases),
             color = NA,
             fillOpacity = .5,
             radius = ~cases/3,
             label = ~cases)

Web based data

USGS Gage near UCSB: ID-11120000

https://waterdata.usgs.gov/monitoring-location/11120000/

Network Linked Data (GeoJSON)

https://labs.waterdata.usgs.gov/api/nldi/linked-data/nwissite/USGS-11120000/navigate/UT

Trance the Upper Tributary (UT) of the USGS-11120000

Adding “Web data” to the map

# site ID
id = "11120000"

# base URL
base ="https://labs.waterdata.usgs.gov/api/nldi/linked-data/"

# Reading sf for URLs in line
leaflet() %>% 
  addProviderTiles(providers$CartoDB) %>% 
  addPolylines(data = read_sf(paste0(base,'nwissite/USGS-',id,'/navigate/UT'))) %>% 
  addPolygons(data =  read_sf(paste0(base,'nwissite/USGS-',id,'/basin')), 
              fillColor =  "transparent", color = "black")

WMS Tiles

WMS tiles can be added directly to a map. Here we use the NEXRAD rainfall information (refelctivity) from the Iowa Mesonet Program

(You may need to scroll out to find an)

conus = filter(us_states(), !stusps %in% c("AK", "PR", "HI"))

leaflet() %>% 
  addProviderTiles(providers$CartoDB) %>% 
  addPolygons(data = st_union(conus), fillColor = "transparent",
              color = "black", weight = 1) %>% 
  addWMSTiles(
    "http://mesonet.agron.iastate.edu/cgi-bin/wms/nexrad/n0r.cgi",
    layers = "nexrad-n0r-900913",
    options = WMSTileOptions(format = "image/png", transparent = TRUE)
  )

Layer Controls

Uses Leaflet’s built-in layers control you can choose one of several base layers, and any number of overlay layers to view.

By defining groups, you have the ability to toogle layers, and overlays on/off.

leaflet() %>% 
  addProviderTiles(providers$CartoDB, group = "Grayscale") %>% 
  addProviderTiles(providers$Esri.WorldTerrain, group = "Terrain") %>% 
  addPolylines(data = read_sf(paste0(base,'nwissite/USGS-',id,'/navigate/UT'))) %>% 
  addPolygons(data =  read_sf(paste0(base,'nwissite/USGS-',id,'/basin')), fillColor =  "transparent", color = "black", group = "basin") %>% 
  addWMSTiles("http://mesonet.agron.iastate.edu/cgi-bin/wms/nexrad/n0r.cgi", layers = "nexrad-n0r-900913",
    options = WMSTileOptions(format = "image/png", transparent = TRUE)) %>% 
  addLayersControl(overlayGroups = c("basin"), baseGroups = c("Terrain", "Grayscale"))

‘Function-ize’

You can wrap your mapping code in functions to allow reusability

watershed_map = function(gage_id){
leaflet() %>% 
  addProviderTiles(providers$CartoDB) %>% 
  addPolylines(data = read_sf(paste0(base,'nwissite/USGS-',gage_id,'/navigate/UT'))) %>% 
  addPolygons(data =  read_sf(paste0(base,'nwissite/USGS-',gage_id,'/basin')), 
              fillColor =  "transparent", color = "black", group = "basin") %>% 
  addWMSTiles("http://mesonet.agron.iastate.edu/cgi-bin/wms/nexrad/n0r.cgi", layers = "nexrad-n0r-900913",
    options = WMSTileOptions(format = "image/png", transparent = TRUE))
}

watershed_map("07382000")

Adding Details

Measures, graticules, and inset maps

watershed_map("07382000") %>% 
  addMeasure() %>% 
  addGraticule() %>% 
  addMiniMap()

leafem (new library)

Home buttons and Mouse Coordinates
Support for raster and stars objects (to come)

watershed_map("07382000") %>% 
  addMeasure() %>% 
  addGraticule() %>% 
  leafem::addHomeButton(group = "basin") %>% 
  leafem::addMouseCoordinates()

leafpop (new library)

Popup Tables

leaflet(data = starbucks) %>% 
  addProviderTiles(providers$CartoDB) %>% 
  addCircleMarkers(
    color = ~pal(city), 
    fillOpacity = .5,
    stroke = FALSE, 
    popup = leafpop::popupTable(starbucks)
  )

Making that table nicer…

leaflet(data = starbucks) %>% addProviderTiles(providers$CartoDB) %>% 
  addCircleMarkers(
    color = ~pal(city), fillOpacity = .5,
    stroke = FALSE, 
    popup = leafpop::popupTable(st_drop_geometry(starbucks[,1:5]), feature.id = FALSE, row.numbers = FALSE)
  )

Mapview

easy, but less control
Support for raster and stars objects (to come in our class)
Implements many of the leafem and leafpop functionalities

library(mapview)
mapview(starbucks)

Geography 176A

Leaflet R

Mike Johnson

Basic Usage

Basemaps

Default (OpenStreetMap) Tiles

Third-Party Tiles

CartoDB

Stamen.Toner

ESRI Imagery

Stamen.TopOSMRelief

Combining Tile Layers

Markers

Example Starbucks data:

Markers

Awesome Icons

Custom Popups

Circle Markers

Marker Clustering

Adding color ramps

Shapes (Polylines, Polygons, Circles)

Getting some data …

Polygons

Circles

Web based data

USGS Gage near UCSB: ID-11120000

Network Linked Data (GeoJSON)

Adding “Web data” to the map

WMS Tiles

Layer Controls

‘Function-ize’

Adding Details

leafem (new library)

leafpop (new library)

Mapview